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from tabnanny import filename_only
import numpy as np
import cv2
import os
import PIL
import torch
from .dataset import Dataset
class ImageDataset(Dataset):
def __init__(self,
img_path, # Path to images.
resolution = None, # Ensure specific resolution, None = highest available.
**super_kwargs, # Additional arguments for the Dataset base class.
):
self.sz = resolution
self.img_path = img_path
self._type = 'dir'
self.files = []
self.idx = 0
self._all_fnames = [os.path.relpath(os.path.join(root, fname), start=self.img_path) for root, _dirs, files in os.walk(self.img_path) for fname in files]
PIL.Image.init()
self._image_fnames = sorted(os.path.join(self.img_path,fname) for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
if len(self._image_fnames) == 0:
raise IOError('No image files found in the specified path')
self.files = []
for f in self._image_fnames:
if not '_mask' in f:
self.files.append(f)
self.files = sorted(self.files)
def __len__(self):
return len(self.files)
@staticmethod
def _file_ext(fname):
return os.path.splitext(fname)[1].lower()
def _load_image(self, fn):
return PIL.Image.open(fn).convert('RGB')
def _get_image(self, idx):
# imgfn, seg_map, img_id = self.data_reader.get_image(idx)
fname = self.files[idx]
ext = self._file_ext(fname)
mask = np.array(self._load_image(fname.replace(ext, f'_mask{ext}')).convert('L')) / 255
mask = cv2.resize(mask,
(self.sz, self.sz), interpolation=cv2.INTER_NEAREST)
rgb = np.array(self._load_image(fname)) # uint8
rgb = cv2.resize(rgb,
(self.sz, self.sz), interpolation=cv2.INTER_AREA)
return rgb, fname.split('/')[-1].replace(ext, ''), mask
def __getitem__(self, idx):
rgb, fname, mask = self._get_image(idx) # modal, uint8 {0, 1}
rgb = rgb.transpose(2,0,1)
mask_tensor = torch.from_numpy(mask).to(torch.float32)
mask_tensor = mask_tensor.unsqueeze(0)
rgb = torch.from_numpy(rgb.astype(np.float32))
rgb = (rgb.to(torch.float32) / 127.5 - 1)
rgb_erased = rgb.clone()
rgb_erased = rgb_erased * (1 - mask_tensor) # erase rgb
rgb_erased = rgb_erased.to(torch.float32)
return rgb, rgb_erased, mask_tensor, fname
def collate_fn(data):
"""Creates mini-batch tensors from the list of images.
We should build custom collate_fn rather than using default collate_fn,
because merging caption (including padding) is not supported in default.
Args:
data: list
- image: torch tensor of shape (3, 256, 256).
Returns:
images: torch tensor of shape (batch_size, 3, 256, 256).
"""
rgbs, rgbs_erased, mask_tensors, fnames = zip(*data)
rgbs = list(rgbs)
rgbs_erased = list(rgbs_erased)
mask_tensors = list(mask_tensors)
fnames = list(fnames)
return torch.stack(rgbs, dim=0), torch.stack(rgbs_erased, dim=0), torch.stack(mask_tensors, dim=0), fnames
def get_loader(img_path, resolution):
"""Returns torch.utils.data.DataLoader for custom coco dataset."""
ds = ImageDataset(img_path=img_path, resolution=resolution)
data_loader = torch.utils.data.DataLoader(dataset=ds,
batch_size=1,
shuffle=False,
num_workers=1,
collate_fn=collate_fn)
return data_loader |